Advertisement

Applied Health Economics and Health Policy

, Volume 12, Issue 6, pp 623–633 | Cite as

The Importance of Proximity to Death in Modelling Community Medication Expenditures for Older People: Evidence From New Zealand

  • Patrick V. MooreEmail author
  • Kathleen Bennett
  • Charles Normand
Original Research Article

Abstract

Background

Concerns about the long-term sustainability of health care expenditures (HCEs), particularly prescribing expenditures, has become an important policy issue in most developed countries. Previous studies suggest that proximity to death (PTD) has a significant effect on total HCEs, with its exclusion leading to an overestimation of likely growth. There are limited studies of pharmaceutical expenditures in which PTD is taken into account.

Objective

This study presents an empirical analysis of public medication expenditure on older individuals in New Zealand (NZ). The aim of the study was to examine the individual effects of age and PTD using individual-level data.

Methods

This study uses individual-level dispensing data from 2008/2009 covering the whole population of medication users aged 70 years or older and resident in NZ. A case–control methodology was used to examine individual cost and medication use for a 12-month period for decedents (cases) and survivors (controls). A random effects two-part model, with a Probit and generalized linear model (GLM) was used to explore the effect of age and PTD on expenditures.

Results

The impact of PTD on prescription expenditure is not as dramatic as studies reporting on acute and/or long-term care. The 12-month decedent-to-survivor mean expenditure ratio was 1.95; 2.09 for males and 1.82 for females. The additional cost of dying in terms of prescription drugs decreases with age, with those who die at 90 years of age or older consuming fewer drugs on average and having a lower mean expenditure than those who died in their 70s and 80s. The following variables were found to have a decreasing effect on the mean monthly prescription expenditures: a reduction of 2.2 % for each additional year of age, 4.2 % being in the Maori ethnic group, and 7.8 % for Pacific Islanders. Increases in monthly expenditure were associated with being a decedent 32.1–62.6 % (depending on month), being of Asian origin 16.2 %, or being a male 12.6 %.

Conclusions

Given the variance reported between survivors and decedents, future projections should include PTD in their models to improve accuracy. Policies targeted at reducing expenditures should not focus on age but on ensuring appropriate and cost-effective prescribing, particularly towards the end of life.

Keywords

Health Care Expenditure Monthly Expenditure Total Health Care Cost Total Health Care Expenditure Coarsen Exact Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Data were provided by the Ministry of Health New Zealand. The initial database was set up using facilities provided by the Trinity Centre for High Performance Computing, which is funded through grants from the Science Foundation Ireland. Patrick V. Moore is funded by the Health Research Board in Ireland (HRB PhD Scholars Programme in Health Services Research) under grant no. PHD/2007/16.

Conflicts of interest

No conflicts to be declared.

Author contributions

Patrick V. Moore designed the study, conducted the data analysis and prepared the manuscript. Kathleen Bennett was involved in the study design and preparation of the manuscript. Charles Normand was involved in the study design and review of the manuscript. All authors performed a critical review of the manuscript content and approved its final version. Patrick V. Moore acts as guarantor of the overall content of this article.

References

  1. 1.
    Statistics New Zealand. National population projections: 2009 (base)–2061. Wellington: Statistics New Zealand. 2009. Available from: http://www.stats.govt.nz/browse_for_stats/population/estimates_and_projections/NationalPopulationProjections_HOTP09base-61.aspx. Accessed 27 Oct 2012.
  2. 2.
    Labour force statistics: population projections, OECD employment and labour market statistics (database). 2011. Available from: http://www.oecd-ilibrary.org/employment/data/labour-force-statistics/historical-population-data-and-projections_data-00538-en;jsessionid=ba3jrdodrsu7f.delta?isPartOf=/content/datacollection/lfs-lfs-data-en. Accessed 21 May 2013.
  3. 3.
    World population prospects: the 2012 revision. United Nations. 2013. Available from: http://esa.un.org/unpd/wpp/index.htm. Accessed 1 Oct 2013.
  4. 4.
    Miller T. Increasing longevity and medicare expenditures. Demography. 2001;38(2):215–26.PubMedCrossRefGoogle Scholar
  5. 5.
    Dormont B, Grignon M, Huber H. Health expenditure growth: reassessing the threat of ageing. Health Econ. 2006;15(9):947–63.PubMedCrossRefGoogle Scholar
  6. 6.
    Fuchs VR. “Though much is taken”: reflections on ageing, health and medical care. Milbank Mem Fund Q Health Soc. 1984;61(1):143–66.Google Scholar
  7. 7.
    Scitovsky AA. “The high cost of dying” revisited. Milbank Q. 1994;72(4):561–91.PubMedCrossRefGoogle Scholar
  8. 8.
    Comas-Herrera A, Wittenberg R, Pickard L, Knapp M. Cognitive impairment in older people: future demand for long-term care services and the associated costs. Int J Geriatr Psychiatry. 2007;22:1037–45.PubMedCrossRefGoogle Scholar
  9. 9.
    Werblow A, Felder S, Zweifel P. Population ageing and health care expenditure: a school of ‘red herrings’? Health Econ. 2007;16(10):1109–26.PubMedCrossRefGoogle Scholar
  10. 10.
    Zweifel P, Felder S, Meiers M. Ageing of population and health care expenditure: a red herring? Health Econ. 1999;8(6):485–96.PubMedCrossRefGoogle Scholar
  11. 11.
    Seshamani M, Gray AM. A longitudinal study of the effects of age and time to death on hospital costs. J Health Econ. 2004;23(2):217–35.Google Scholar
  12. 12.
    Colombier C, Weber W. Projecting health-care expenditure for Switzerland: further evidence against the ‘red-herring’ hypothesis. Int J Health Plan Manage. 2011;26(3):246–63. doi: 10.1002/hpm.1068.CrossRefGoogle Scholar
  13. 13.
    de Meijer C, Koopmanschap M, d’ Uva TB, van Doorslaer E. Determinants of long-term care spending: age, time to death or disability? J Health Econ. 2011;30(2):425–38.PubMedCrossRefGoogle Scholar
  14. 14.
    van Baal PH, Wong A. Time to death and the forecasting of macro-level health care expenditures: some further considerations. J Health Econ. 2012;31(6):876–87.PubMedCrossRefGoogle Scholar
  15. 15.
    Healthdata. Organisation for Economic Co-operation and Development. 2013. Accessed 30 Sep 2013.Google Scholar
  16. 16.
    Seshamani M, Gray AM. Time to death and health expenditure: an improved model for the impact of demographic change on health care costs. Age Ageing. 2004;33(6):556–61.PubMedCrossRefGoogle Scholar
  17. 17.
    Kildemoes H, Christiansen T, Gyrd-Hansen D. The impact of population ageing on future Danish drug expenditure. Health Policy. 2006;75:298–311.PubMedCrossRefGoogle Scholar
  18. 18.
    OECD. Health at a glance 2011: OECD indicators. 2011. Available from: http://www.oecd.org/els/health-systems/health-at-a-glance.htm.
  19. 19.
    Iacus SM, King G, Porro G. Multivariate matching methods that are monotonic imbalance bounding. J Am Stat Assoc. 2011;106(493):345–61.CrossRefGoogle Scholar
  20. 20.
    Blough DK, Madden CW, Hornbrook MC. Modeling risk using generalized linear models. J Health Econ. 1999;18(2):153–71.PubMedCrossRefGoogle Scholar
  21. 21.
    Manning W, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461–94.PubMedCrossRefGoogle Scholar
  22. 22.
    Duan N. Smearing estimate: a non-parametric retransformation method. J Am Stat Assoc. 1983;78:605–10.CrossRefGoogle Scholar
  23. 23.
    Barber J, Thompson S. Analysis and interpretation of cost data in randomised controlled trials: review of published studies. BMJ. 1998;317:1195–200.PubMedCentralPubMedCrossRefGoogle Scholar
  24. 24.
    Barber J, Thompson S. Analysis of cost data in randomised controlled trials: an application of the non-parametric bootstrap. Med Stat. 2000;19(23):3219–36.CrossRefGoogle Scholar
  25. 25.
    Duan N, Manning WG, Morris CN, Newhouse JP. A comparison of alternative models of the demand for medical care. J Bus Econ Stat. 1983;1(2):115–26.Google Scholar
  26. 26.
    Jones AM. Health econometrics. In: Culyer A, Newhouse J, editors. Handbook of health economics. Amsterdam: Elsevier; 2000.Google Scholar
  27. 27.
    Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F, editors. Second international symposium on information theory. Budapest: Akademiai Kiado; 1973: pp. 267–281.Google Scholar
  28. 28.
    Barber J, Thompson S. Multiple regression of cost data: use of generalised linear models. J Health Serv Res Policy. 2004;9(4):197–204. doi: 10.1258/1355819042250249.PubMedCrossRefGoogle Scholar
  29. 29.
    Bjørner TB, Arnberg S. Terminal costs, improved life expectancy and future public health expenditure. Int J Health Care Finance Econ. 2012;12:129–43.PubMedCrossRefGoogle Scholar
  30. 30.
    Aspin C, Jowsey T, Glasgow N, Dugdale P, Nolte E, O’Hallahan J, et al. Health policy responses to rising rates of multi-morbid chronic illness in Australia and New Zealand. Aust NZ J Public Health. 2010;34(4):386–93.CrossRefGoogle Scholar
  31. 31.
    McGrail K, Green B, Barer ML, Evans RG, Hertzman C, Normand C. Age, costs of acute and long-term care and proximity to death: evidence for 1987–88 and 1994–95 in British Columbia. Age Ageing. 2000;29(3):249–53.PubMedCrossRefGoogle Scholar
  32. 32.
    Lubitz J, Beebe J, Baker C. Longevity and medicare expenditures. N Engl J Med. 1995;332(15):999–1002.PubMedCrossRefGoogle Scholar
  33. 33.
    Angulo AM, Barberán R, Egea P, Mur J. An analysis of health expenditure on a microdata population basis. Econ Model. 2011;28:169–80.CrossRefGoogle Scholar
  34. 34.
    Yang Z, Norton EC, Stearns SC. Longevity and health care expenditures: the real reasons older people spend more. J Gerontol B Psychol Sci Soc Sci. 2003;58(1):S2–10.PubMedCrossRefGoogle Scholar
  35. 35.
    Felder S, Werblow A, Zweifel P. Do red herrings swim in circles? Controlling for the endogeneity of time to death. J Health Econ. 2010;29(2):205–12.PubMedCrossRefGoogle Scholar
  36. 36.
    Blakely T, Fawcett J, Atkinson J, Tobias M, Cheung J. Decades of disparity II: socioeconomic mortality trends in New Zealand, 1981–1999. Wellington: Ministry of Health; 2005.Google Scholar
  37. 37.
    Ministry of health. Tatau Kura Tangata: Health of Older Maori Chart Book 2011 Wellington: Ministry of Health (NZ); 2011.Google Scholar
  38. 38.
    McNaughton H, Weatherall M, McPherson K, Taylor W, Harwood M. The comparability of resource utilisation for Europeans and non-Europeans following stroke in New Zealand. NZ Med J. 2002;115(1149):101–3.Google Scholar
  39. 39.
    Scott K, Marwick J, Crampton P. Utilization of general practitioner services in New Zealand and its relationship with income, ethnicity and government subsidy. Health Serv Manage Res. 2003;16(1):45–55.PubMedCrossRefGoogle Scholar
  40. 40.
    Crimmins EM, Saito Y, Ingegnenri D. Trends in disability-free life expectancy in the United States. Popul Dev Rev. 1997;23:555–72.CrossRefGoogle Scholar
  41. 41.
    Waidmann TA, Liu K. Disability trends among elderly persons and implications for the future. J Gerontol. 2000;55:S298–307.CrossRefGoogle Scholar
  42. 42.
    Statistics New Zealand. New Zealand period life tables: 2010–12. Wellington: Statistics New Zealand; 2013. Available from: http://www.stats.govt.nz/browse_for_stats/health/life_expectancy/NZLifeTables_HOTP10-12.aspx. Accessed 20 Apr 2013.
  43. 43.
    Gandjour A, Lauterbach KW. Does prevention save costs? Considering deferral of the expensive last year of life. J Health Econ. 2005;24(4):715–24.PubMedCrossRefGoogle Scholar
  44. 44.
    Gandjour A. Aging diseases: do they prevent preventive health care from saving costs? Health Econ. 2009;18(3):355–62.PubMedCrossRefGoogle Scholar
  45. 45.
    Broad JB, Ashton T, Lumley T, Connolly MJ. Reports of the proportion of older people living in long-term care: a cautionary tale from New Zealand. Aust NZ J Public Health. 2013;27(3):264–71.CrossRefGoogle Scholar
  46. 46.
    Spillman BC, Lubitz J. The effect of longevity on spending for acute and long-term care. N Engl J Med. 2000;342(19):1409–15.PubMedCrossRefGoogle Scholar
  47. 47.
    Statistics New Zealand. National population estimates 2008. Wellington: Statistics New Zealand; 2011. Available from: http://www.stats.govt.nz/browse_for_stats/population/estimates_and_projections/national-pop-estimates.aspx. Accessed 21 Jun 2011.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Patrick V. Moore
    • 1
    Email author
  • Kathleen Bennett
    • 2
  • Charles Normand
    • 1
  1. 1.Centre for Health Policy and ManagementTrinity College DublinDublin 2Ireland
  2. 2.Trinity Centre for Health SciencesSt James’s HospitalDublin 8Ireland

Personalised recommendations